In the realm of biomedicine, privacy-preserving AI methodologies are pivotal for safeguarding sensitive information while harnessing the capabilities of artificial intelligence. These methodologies play a significant role in mitigating privacy-related apprehensions when training AI models on delicate biomedical data. The field has recently seen progress with strategies such as federated machine learning that facilitates cooperative learning without the need to share raw data, thereby maintaining privacy. Merging these methods with other privacy-preserving techniques can offer effective privacy assurances in a decentralized fashion for biomedical applications. Initiatives in this domain strive to strike a balance between leveraging AI’s advantages in healthcare and preserving patient confidentiality, thus promoting collaborative research and scientific advancement.
